5 citations · 12 across the 17 of their papers we have counts for
17 papers
BiRoAD: Learning Shared and Role-Adaptive Representations for Bimanual Manipulation
Yan Shen, Yuchen Liu, Feng Jiang +5
Bimanual manipulation requires policies that coordinate two arms while adapting their functional roles to scene geometry, object configuration, and task context. Learning such scen…
Scaling Bimanual Household Manipulation from 1,500 hours of Demonstrations to On-Policy Corrections
Jiafeng Xu, Qi Li, Yan Shen +7
Learning generalist policies for robust bimanual manipulation is bottlenecked by the scarcity of high quality large scale human demonstration data. In this work, we release 1,500 h…
LeHome: A Simulation Environment for Deformable Object Manipulation in Household Scenarios
Zeyi Li, Yushi Yang, Shawn Xie +15
Household environments present one of the most common, impactful yet challenging application domains for robotics. Within household scenarios, manipulating deformable objects is pa…
BiPreManip: Learning Affordance-Based Bimanual Preparatory Manipulation through Anticipatory Collaboration
Yan Shen, Feng Jiang, Zichen He +5
Many everyday objects are difficult to directly grasp (e.g., a flat iPad) or manipulate functionally (e.g., opening the cap of a pen lying on a desk). Such tasks require sequential…
A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation
Jiaqi Liang, Yue Chen, Qize Yu +4
Furniture assembly is a crucial yet challenging task for robots, requiring precise dual-arm coordination where one arm manipulates parts while the other provides collaborative supp…
Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation
Liang Heng, Jiadong Xu, Yiwen Wang +6
Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric reasoning. Existing approaches e…
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
Mingxu Zhang, Xiaoqi Li, Jiahui Xu +5
Recent advancements in 3D robotic manipulation have improved grasping of everyday objects, but transparent and specular materials remain challenging due to depth sensing limitation…
BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
Yan Shen, Ruihai Wu, Yubin Ke +6
Shape assembly, the process of combining parts into a complete whole, is a crucial robotic skill with broad real-world applications. Among various assembly tasks, geometric assembl…
CrayonRobo: Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation
Xiaoqi Li, Lingyun Xu, Mingxu Zhang +8
In robotic, task goals can be conveyed through various modalities, such as language, goal images, and goal videos. However, natural language can be ambiguous, while images or video…
AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning
Yuanfei Wang, Xiaojie Zhang, Ruihai Wu +6
Articulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated ob…
GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation
Haoran Lu, Ruihai Wu, Yitong Li +7
Manipulating garments and fabrics has long been a critical endeavor in the development of home-assistant robots. However, due to complex dynamics and topological structures, garmen…
Broadcasting Support Relations Recursively from Local Dynamics for Object Retrieval in Clutters
Yitong Li, Ruihai Wu, Haoran Lu +4
In our daily life, cluttered objects are everywhere, from scattered stationery and books cluttering the table to bowls and plates filling the kitchen sink. Retrieving a target obje…
NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulation
Ran Xu, Yan Shen, Xiaoqi Li +2
Enabling home-assistant robots to perceive and manipulate a diverse range of 3D objects based on human language instructions is a pivotal challenge. Prior research has predominantl…
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
Xiaoqi Li, Mingxu Zhang, Yiran Geng +6
Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However, learning-based robot manipulation, trained on…
Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations
Yushi Du, Ruihai Wu, Yan Shen +1
Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom and are rich in geometr…
ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection
Xiaoqi Li, Yanzi Wang, Yan Shen +6
In the realm of future home-assistant robots, 3D articulated object manipulation is essential for enabling robots to interact with their environment. Many existing studies make use…
Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
Ruihai Wu, Kai Cheng, Yan Shen +3
Perceiving and manipulating 3D articulated objects in diverse environments is essential for home-assistant robots. Recent studies have shown that point-level affordance provides ac…